AI and Robotics (STAR lab)

We are the Surrey Technology for Autonomous systems and Robotics (STAR) Lab led by Professor Yang Gao.

A team of academic scholars, roboticists and computer scientists. It is our mission to build on the Surrey Space Centre (SSC) heritage of 'small-sat' engineering approach and extend this philosophy to advance autonomous systems and robotics for space.

Based on Dual Reciprocating Drilling (DRD) which does not reply on overhead force to operate

Lighter weight, higher power efficiency than conventional drilling such as rotary and percussive

Advantageous for low-gravity mission senarios such as the Moon, asteroids and comets.

The probe (~10kg) free falls and penetrates into target planetary bodies at hundreds of metres per second, and a small angle of attack (less than 8 deg) is permitted

Fully instrumented with engineering and scientific payload

Involved in MoonLITE, LunarEX/NET missions and the UK Penetrator Consortium.

Space and non-space missions

The ExoMars mission aims to demonstrate key planetary robotics technologies such as rover autonomy and deep drilling. The STAR Lab was involved in the Phase A study, has been involved in the UK PanCam payload studies, and developing next generation rover autonomy and planetary drilling technologies, as well as advanced rover locomotion technologies in collaboration with Airbus DS.

The follow-on studies have been looking at next-generation GNC for planetary rover to achieve faster travelling speed and longer traverse distance based on higher autonomy capabilities through robotic vision and machine learning techniques. Future missions that will benefit from these advancement include Mars or Phobos sample return mission.

The Proba3 mission aims to demonstrate key technologies that enable precision formation flying between two spacecraft for the first time, such as high accuracy measurement system.

The STAR Lab has been involved in the calibration of the Lateral and Longitudinal Sensor (FLLS) onboard one of the Proba3 spacecraft and its ground testing. FLLS could allow large-scale structures to be deployed and maintained in space, monitoring structural distortion before, during and after deployment, and providing in-flight corrections to data collection.

Examples of such future applications include in-orbit observatories, positioning of telecommunication satellite antennas, and deployable mechanisms on Moon or Mars missions.

The LunaResurs mission aims to land at the lunar south pole to search for minerals and water. The STAR Lab is involved in developing the landing sensor called LEIA, a LIDAR instrument that enables the lander to avioid uneven terrain and land safely.

The LEIA (or LIDAR for Extra-terrestrial Imaging Applications) will provide a 3D map of the lunar surface from two altitudes during landing: 1.3 km and 250 m, with resolutions of 1.0 m and 0.1 m respectively. Due to launch in 2021, the LunaResurs will be the first mission applies most of the components in the LIDAR in space or on the lunar surface.

Subject to a rigorous test campaign, this mission will pave the way for more extensive applications of LIDAR technologies in future space missions.

The STAR Lab has been transferring space robotics and autonomous systems technologies into the nuclear sector, by developing:

Reconfigurable autonomous software archtecture for operation of nuclear plants where design requirements and challenges are similar to relevant space applications

Customised robotic vision system for autonomous inspection and classification of nuclear waste going through the process of ‘sort-and-segregate’

Customised machine vision system for autonomously detection, measurement and tracking of the dynamic behaviours of the gas bubbles of the liquid pond in nuclear decomissioning sites.

The STAR Lab has been transferring and integrating space robotics and autonomou systems technologies into the agriculture sector, by developing:

Computer vision techniques to detect anomalies in crops and related agricultural applications including spectrum based segmentation and automatic selection using clustering algorithm. For examples, leaf close up image is segmented and analysed whereby diseased sections are identified and extracted and diseased / Healthy leaf metrics are calculated